Phonics

About Course

Data Science is the dynamic field that extracts valuable insights from data using a blend of advanced algorithms, statistics, and domain knowledge. In a world driven by data, Data Scientists analyze, visualize, and interpret massive datasets to uncover patterns, trends, and meaningful information. By applying statistical analysis, machine learning, and data visualization techniques, Data Science empowers decision-making across industries, from healthcare and finance to marketing and beyond. As a data-driven revolution sweeps through various sectors, Data Science emerges as the key to unlocking the hidden potential within data, driving innovation, and shaping the way we understand the world around us.

About Winquest

WinQuest’s Data Science classes for kids offer a dynamic learning experience in the world of data analysis and interpretation. Our courses provide flexible timing, ensuring convenience for young learners. Through live and interactive classes, students engage with experienced instructors to explore data science concepts. They develop essential skills in data handling, analysis, and visualization, all while benefiting from valuable student-teacher interaction. These classes empower kids to understand and harness the power of data, fostering critical thinking and problem-solving abilities that are highly relevant in today’s data-driven world.

Course Content

Prerequisites

Proficiency in Python programming.
Understanding of basic programming concepts (variables, loops, functions).
Familiarity with data structures (lists, dictionaries).
Basic knowledge of mathematical concepts (algebra, statistics).

Requirements

A laptop or desktop to connect.
Stable internet connection.
Python (3.6 or higher) with libraries: NumPy, Pandas, Matplotlib, Seaborn, scikit-learn.
Jupyter Notebook for interactive coding and documentation.

Course Content:

Age Group: 13+ Duration: 25 hours
Units	Description
Lesson 1: What is Data Science?	Understanding the role of data science in real-world applications. Introducing the data science life cycle.
Lesson 2: Python for Data Science	Brief Python recap. Overview of essential libraries: NumPy, Pandas, Matplotlib.
Lesson 3: Data Collection and Sources	Exploring different data sources: CSV, APIs, databases. Retrieving data using libraries like requests or pandas.
Lesson 4: Data Cleaning and Preprocessing	Identifying and handling missing values, outliers, and inconsistencies. Transforming and shaping data for analysis.
Lesson 5: Data Visualization	Creating meaningful visualizations using Matplotlib and Seaborn. Analyzing distributions, trends, and relationships.
Lesson 6: Statistical Analysis	Basic statistical concepts: mean, median, variance, etc. Hypothesis testing and interpreting results.
Lesson 7: Introduction to Machine Learning	Overview of supervised and unsupervised learning. Choosing the right algorithm for the task.
Lesson 8: Model Training and Evaluation	Splitting data into training and testing sets. Evaluating model performance with metrics like accuracy and confusion matrix.
Lesson 9: Feature Engineering	Selecting and transforming features for improved model performance. Dealing with categorical data and encoding techniques.
Lesson 10: Dimensionality Reduction	Reducing data dimensions using techniques like PCA. Visualizing high-dimensional data in lower dimensions.
Lesson 11: Time Series Analysis	Understanding time-dependent data. Techniques for analyzing and forecasting time series data.
Lesson 12: Project 1 – Exploring a Real Dataset	Students choose a real-world dataset and apply data cleaning, EDA, and basic analysis.
Lesson 13: Project 2 – Predictive Modeling	Students build a predictive model using machine learning algorithms. Documenting the entire process, from data preparation to model evaluation.